https://github.com/cvi-szu/qa-clims
공식 코드Question-Answer Cross Language Image Matching for Weakly Supervised Semantic Segmentation
Papers with Code 아카이브
Contrastive Learning
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Contrastive Learning is a deep learning technique for unsupervised representation learning. The goal is to learn a representation of data such that similar instances are close together in the representation space, while dissimilar instances are far apart.
It has been shown to be effective in various computer vision and natural language processing tasks, including image retrieval, zero-shot learning, and cross-modal retrieval. In these tasks, the learned representations can be used as features for downstream tasks such as classification and clustering.
<span style="color:grey; opacity: 0.6">(Image credit: Schroff et al. 2015)</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/cviu-csu/tct-infonce
공식 코드https://github.com/cvl-umass/PARTICLE
공식 코드PARTICLE: Part Discovery and Contrastive Learning for Fine-grained Recognition
https://github.com/cvlab-columbia/zsrobust4foundationmodel
공식 코드Understanding Zero-Shot Adversarial Robustness for Large-Scale Models
https://github.com/cvlab-stonybrook/c-rex
공식 코드Improving Contrastive Learning for Referring Expression Counting
https://github.com/cvlab-stonybrook/plm_ssl
공식 코드Precise Location Matching Improves Dense Contrastive Learning in Digital Pathology
https://github.com/cvmi-lab/pla
공식 코드https://github.com/cvml-cfu/ecam
공식 코드ECAM: A Contrastive Learning Approach to Avoid Environmental Collision in Trajectory Forecasting
https://github.com/cwei01/fairdgcl
공식 코드FairDgcl: Fairness-aware Recommendation with Dynamic Graph Contrastive Learning
https://github.com/cxa9264/confeti
공식 코드https://github.com/cyh-0/CAVP
공식 코드Unraveling Instance Associations: A Closer Look for Audio-Visual Segmentation
https://github.com/cyno2232/cmgn
공식 코드https://github.com/cys1102/fba-net
공식 코드https://github.com/czbiohub-sf/napari-iohub
공식 코드Contrastive learning of cell state dynamics in response to perturbations
https://github.com/czhaneva/skelemixclr
공식 코드https://github.com/d10andy/sad
공식 코드https://github.com/d3mlab/rir
공식 코드Self-Supervised Contrastive BERT Fine-tuning for Fusion-based Reviewed-Item Retrieval
https://github.com/d3mlab/rir_data
공식 코드Self-Supervised Contrastive BERT Fine-tuning for Fusion-based Reviewed-Item Retrieval
https://github.com/dahiyaaneesh/peclr
공식 코드https://github.com/daisy-zzz/actol
공식 코드Provable Ordering and Continuity in Vision-Language Pretraining for Generalizable Embodied Agents
https://github.com/daisy-zzz/ppcl
공식 코드Contrastive Learning for Implicit Social Factors in Social Media Popularity Prediction
https://github.com/daniellin97/aclr4rumor-naacl2022
공식 코드Detect Rumors in Microblog Posts for Low-Resource Domains via Adversarial Contrastive Learning
https://github.com/danieltrosten/deepmvc
공식 코드On the Effects of Self-supervision and Contrastive Alignment in Deep Multi-view Clustering
https://github.com/danikiyasseh/CLOCS
공식 코드CLOCS: Contrastive Learning of Cardiac Signals Across Space, Time, and Patients
https://github.com/danishgufran/STELLAR
공식 코드https://github.com/daod/coca
공식 코드Contrastive Learning of User Behavior Sequence for Context-Aware Document Ranking
https://github.com/dariabroscoteanu/roclico
공식 코드https://github.com/darlinginthesv/probabilistic-prototypical-pixel-contrast
공식 코드https://github.com/darrenzhang01/excon
공식 코드ExCon: Explanation-driven Supervised Contrastive Learning for Image Classification
https://github.com/data-designer/da
공식 코드Bi-discriminator Domain Adversarial Neural Networks with Class-Level Gradient Alignment
https://github.com/datadrivenwheels/scvlm
공식 코드ScVLM: Enhancing Vision-Language Model for Safety-Critical Event Understanding
https://github.com/david-li0406/script-based-character-understanding
공식 코드Multi-level Contrastive Learning for Script-based Character Understanding
https://github.com/davidczy/supcon_gamma
공식 코드https://github.com/davidpengucf/sfdahpe
공식 코드https://github.com/davidup1/urlbert
공식 코드Continuous Multi-Task Pre-training for Malicious URL Detection and Webpage Classification
https://github.com/ddehun/density
공식 코드DEnsity: Open-domain Dialogue Evaluation Metric using Density Estimation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.